This repository contains the codes for DewarpNet training.
train.txt
& val.txt
. Contents should be like:
1/824_8-cp_Page_0503-7Ns0001
1/824_1-cp_Page_0504-2Cw0001
python trainwc.py --arch unetnc --data_path ./data/DewarpNet/doc3d/ --batch_size 50 --tboard
python trainbm.py --arch dnetccnl --img_rows 128 --img_cols 128 --img_norm --n_epoch 250 --batch_size 50 --l_rate 0.0001 --tboard --data_path ./DewarpNet/doc3d
python infer.py --wc_model_path ./eval/models/unetnc_doc3d.pkl --bm_model_path ./eval/models/dnetccnl_doc3d.pkl --show
We use the same evaluation code as DocUNet. To reproduce the quantitative results reported in the paper use the images available here.
[Important note about Matlab version] We noticed that Matlab 2020a uses a different SSIM implementation which gives a better MS-SSIM score (0.5623). Whereas we have used Matlab 2018b. Please compare the scores according to your Matlab version.
If you use the dataset or this code, please consider citing our work-
@inproceedings{SagnikKeICCV2019,
Author = {Sagnik Das*, Ke Ma*, Zhixin Shu, Dimitris Samaras, Roy Shilkrot},
Booktitle = {Proceedings of International Conference on Computer Vision},
Title = {DewarpNet: Single-Image Document Unwarping With Stacked 3D and 2D Regression Networks},
Year = {2019}}